Evaluation of a forecasting system to facilitate decision-making for the chemical control of Asian soybean rust

Asian soybean rust (ASR; Phakopsora pachyrhizi) is one of the most important diseases of soybean that can reduce soybean yield. Thus, to minimize the number of fungicide sprays in the crop, disease forecasting systems are tools that could assist in the decision-making process regarding the timing for fungicide application. The aim of this study was to develop and validate a forecasting system for ASR to guide chemical control. Four experiments were conducted at three locations (Cerro Largo, Entre-Ijuís and Passo Fundo) in the state of Rio Grande do Sul in southern Brazil. Treatments consisted of intervals of calculated severity values (CSV) between fungicide sprays, in addition to control without spray. Thousand-grain mass and yield (kg ha^-1) were evaluated, along with ASR severity. The data were subjected to analysis of variance using the F-test and the means compared using Tukey's HSD posthoc test at 5% error probability level. Fungicide treatments scheduled based on the disease forecasting system prevented yield loss compared to the untreaded control treatment, while also reducing the number of fungicide sprays relative to a calendar-based schedule. Thus, we conclude that the proposed disease forecasting system is a viable tool for ASR management.